---
title: "DIYtranscriptomics"
output:
flexdashboard::flex_dashboard:
orientation: rows
source_code: embed
---
```{r setup, include=FALSE}
library(flexdashboard)
knitr::opts_chunk$set(warning=FALSE, message=FALSE)
```
Overview {data-icon="fa-signal"}
=====================================
Row {data-height=400}
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### Signal distribution: raw data
```{r raw data}
library(tidyverse) # provides access to Hadley Wickham's collection of R packages for data science, which we will use throughout the course
library(tximport) # package for getting Kallisto results into R
library(ensembldb) #helps deal with ensembl
library(EnsDb.Hsapiens.v86) #replace with your organism-specific database package
targets <- read_tsv("studydesign.txt")# read in your study design
path <- file.path(targets$sample, "abundance.tsv") # set file paths to your mapped data
Tx <- transcripts(EnsDb.Hsapiens.v86, columns=c("tx_id", "gene_name"))
Tx <- as_tibble(Tx)
Tx <- dplyr::rename(Tx, target_id = tx_id)
Tx <- dplyr::select(Tx, "target_id", "gene_name")
Txi_gene <- tximport(path,
type = "kallisto",
tx2gene = Tx,
txOut = FALSE, #determines whether your data represented at transcript or gene level
countsFromAbundance = "lengthScaledTPM",
ignoreTxVersion = TRUE)
library(tidyverse)
library(edgeR)
library(matrixStats)
library(cowplot)
sampleLabels <- targets$sample
myDGEList <- DGEList(Txi_gene$counts)
log2.cpm <- cpm(myDGEList, log=TRUE)
log2.cpm.df <- as_tibble(log2.cpm, rownames = "geneID")
colnames(log2.cpm.df) <- c("geneID", sampleLabels)
log2.cpm.df.pivot <- pivot_longer(log2.cpm.df, # dataframe to be pivoted
cols = HS01:CL13, # column names to be stored as a SINGLE variable
names_to = "samples", # name of that new variable (column)
values_to = "expression") # name of new variable (column) storing all the values (data)
p1 <- ggplot(log2.cpm.df.pivot) +
aes(x=samples, y=expression, fill=samples) +
geom_violin(trim = FALSE, show.legend = FALSE) +
stat_summary(fun = "median",
geom = "point",
shape = 95,
size = 10,
color = "black",
show.legend = FALSE) +
labs(y="log2 expression", x = "sample",
title="Log2 Counts per Million (CPM)",
subtitle="unfiltered, non-normalized",
caption=paste0("produced on ", Sys.time())) +
theme_bw()
cpm <- cpm(myDGEList)
keepers <- rowSums(cpm>1)>=5 #user defined
myDGEList.filtered <- myDGEList[keepers,]
log2.cpm.filtered <- cpm(myDGEList.filtered, log=TRUE)
log2.cpm.filtered.df <- as_tibble(log2.cpm.filtered, rownames = "geneID")
colnames(log2.cpm.filtered.df) <- c("geneID", sampleLabels)
log2.cpm.filtered.df.pivot <- pivot_longer(log2.cpm.filtered.df, # dataframe to be pivoted
cols = HS01:CL13, # column names to be stored as a SINGLE variable
names_to = "samples", # name of that new variable (column)
values_to = "expression") # name of new variable (column) storing all the values (data)
p2 <- ggplot(log2.cpm.filtered.df.pivot) +
aes(x=samples, y=expression, fill=samples) +
geom_violin(trim = FALSE, show.legend = FALSE) +
stat_summary(fun = "median",
geom = "point",
shape = 95,
size = 10,
color = "black",
show.legend = FALSE) +
labs(y="log2 expression", x = "sample",
title="Log2 Counts per Million (CPM)",
subtitle="filtered, non-normalized",
caption=paste0("produced on ", Sys.time())) +
theme_bw()
myDGEList.filtered.norm <- calcNormFactors(myDGEList.filtered, method = "TMM")
log2.cpm.filtered.norm <- cpm(myDGEList.filtered.norm, log=TRUE)
log2.cpm.filtered.norm.df <- as_tibble(log2.cpm.filtered.norm, rownames = "geneID")
colnames(log2.cpm.filtered.norm.df) <- c("geneID", sampleLabels)
log2.cpm.filtered.norm.df.pivot <- pivot_longer(log2.cpm.filtered.norm.df, # dataframe to be pivoted
cols = HS01:CL13, # column names to be stored as a SINGLE variable
names_to = "samples", # name of that new variable (column)
values_to = "expression") # name of new variable (column) storing all the values (data)
p3 <- ggplot(log2.cpm.filtered.norm.df.pivot) +
aes(x=samples, y=expression, fill=samples) +
geom_violin(trim = FALSE, show.legend = FALSE) +
stat_summary(fun = "median",
geom = "point",
shape = 95,
size = 10,
color = "black",
show.legend = FALSE) +
labs(y="log2 expression", x = "sample",
title="Log2 Counts per Million (CPM)",
subtitle="filtered, TMM normalized",
caption=paste0("produced on ", Sys.time())) +
theme_bw()
p1
```
### Signal distribution: filtered and normalized data
```{r processed data}
p3
```
### PCA plot
```{r PCA}
library(tidyverse)
library(DT)
library(gt)
library(plotly)
group <- targets$group
group <- factor(group)
pca.res <- prcomp(t(log2.cpm.filtered.norm), scale.=F, retx=T)
pc.var<-pca.res$sdev^2 # sdev^2 captures these eigenvalues from the PCA result
pc.per<-round(pc.var/sum(pc.var)*100, 1)
pca.res.df <- as_tibble(pca.res$x)
pca.plot <- ggplot(pca.res.df) +
aes(x=PC1, y=PC2, label=sampleLabels, color = group) +
geom_point(size=4) +
stat_ellipse() +
xlab(paste0("PC1 (",pc.per[1],"%",")")) +
ylab(paste0("PC2 (",pc.per[2],"%",")")) +
labs(title="PCA plot",
caption=paste0("produced on ", Sys.time())) +
coord_fixed() +
theme_bw()
ggplotly(pca.plot)
```
Row {data-height=400}
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### Filtered and normalized counts per million (CPM)
```{r filtered and normalized data}
mydata.df <- mutate(log2.cpm.filtered.norm.df,
healthy.AVG = (HS01 + HS02 + HS03 + HS04 + HS05)/5,
disease.AVG = (CL08 + CL10 + CL11 + CL12 + CL13)/5,
#now make columns comparing each of the averages above that you're interested in
LogFC = (disease.AVG - healthy.AVG)) %>% #note that this is the first time you've seen the 'pipe' operator
mutate_if(is.numeric, round, 2)
datatable(mydata.df[,c(1,12:14)],
extensions = c('KeyTable', "FixedHeader"),
filter = 'top',
options = list(keys = TRUE,
searchHighlight = TRUE,
pageLength = 10,
lengthMenu = c("10", "25", "50", "100")))
```
DEGs {data-orientation=columns data-icon="fa-random"}
=====================================
Column {data-width=500}
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### Volcano plot
```{r volcano plot}
library(tidyverse)
library(limma)
library(edgeR)
library(gt)
library(DT)
library(plotly)
group <- factor(targets$group)
design <- model.matrix(~0 + group)
colnames(design) <- levels(group)
v.DEGList.filtered.norm <- voom(myDGEList.filtered.norm, design, plot = FALSE)
fit <- lmFit(v.DEGList.filtered.norm, design)
contrast.matrix <- makeContrasts(infection = disease - healthy,
levels=design)
fits <- contrasts.fit(fit, contrast.matrix)
ebFit <- eBayes(fits)
myTopHits <- topTable(ebFit, adjust ="BH", coef=1, number=40000, sort.by="logFC")
myTopHits.df <- myTopHits %>%
as_tibble(rownames = "geneID")
vplot <- ggplot(myTopHits.df) +
aes(y=-log10(adj.P.Val), x=logFC, text = paste("Symbol:", geneID)) +
geom_point(size=2) +
geom_hline(yintercept = -log10(0.01), linetype="longdash", colour="grey", size=1) +
geom_vline(xintercept = 1, linetype="longdash", colour="#BE684D", size=1) +
geom_vline(xintercept = -1, linetype="longdash", colour="#2C467A", size=1) +
#annotate("rect", xmin = 1, xmax = 12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#BE684D") +
#annotate("rect", xmin = -1, xmax = -12, ymin = -log10(0.01), ymax = 7.5, alpha=.2, fill="#2C467A") +
labs(title="Volcano plot",
subtitle = "Cutaneous leishmaniasis",
caption=paste0("produced on ", Sys.time())) +
theme_bw()
ggplotly(vplot)
```
Column {.tabset data-width=500}
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### DEG table
```{r DEG table}
results <- decideTests(ebFit, method="global", adjust.method="BH", p.value=0.01, lfc=2)
colnames(v.DEGList.filtered.norm$E) <- sampleLabels
diffGenes <- v.DEGList.filtered.norm$E[results[,1] !=0,]
diffGenes.df <- as_tibble(diffGenes, rownames = "geneID")
datatable(diffGenes.df,
extensions = c('KeyTable', "FixedHeader"),
caption = 'Table 1: DEGs in cutaneous leishmaniasis',
options = list(keys = TRUE, searchHighlight = TRUE, pageLength = 10, lengthMenu = c("10", "25", "50", "100"))) %>%
formatRound(columns=c(2:11), digits=2)
```
### heatmap
```{r heatmap}
library(tidyverse)
library(gplots)
library(RColorBrewer)
myheatcolors <- rev(brewer.pal(name="RdBu", n=11))
clustRows <- hclust(as.dist(1-cor(t(diffGenes), method="pearson")), method="complete") #cluster rows by pearson correlation
clustColumns <- hclust(as.dist(1-cor(diffGenes, method="spearman")), method="complete")
module.assign <- cutree(clustRows, k=2)
module.color <- rainbow(length(unique(module.assign)), start=0.1, end=0.9)
module.color <- module.color[as.vector(module.assign)]
heatmap.2(diffGenes,
Rowv=as.dendrogram(clustRows),
Colv=as.dendrogram(clustColumns),
RowSideColors=module.color,
col=myheatcolors, scale='row', labRow=NA,
density.info="none", trace="none",
cexRow=1, cexCol=1, margins=c(8,20))
```
Functional enrichment {data-icon="fa-signal"}
=====================================
Column {.tabset data-width=500}
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### GO - enriched in disease
```{r gostplot1}
modulePick <- 2
myModule_up <- diffGenes[names(module.assign[module.assign %in% modulePick]),]
hrsub_up <- hclust(as.dist(1-cor(t(myModule_up), method="pearson")), method="complete")
library(limma)
library(gplots) #for heatmaps
library(DT) #interactive and searchable tables of our GSEA results
library(GSEABase) #functions and methods for Gene Set Enrichment Analysis
library(Biobase) #base functions for bioconductor; required by GSEABase
library(GSVA) #Gene Set Variation Analysis, a non-parametric and unsupervised method for estimating variation of gene set enrichment across samples.
library(gprofiler2) #tools for accessing the GO enrichment results using g:Profiler web resources
library(clusterProfiler) # provides a suite of tools for functional enrichment analysis
library(msigdbr) # access to msigdb collections directly within R
library(enrichplot) # great for making the standard GSEA enrichment plots
gost.res_up <- gost(rownames(myModule_up), organism = "hsapiens", correction_method = "fdr")
gostplot(gost.res_up, interactive = T, capped = T) #set interactive=FALSE to get plot for publications
```
### GO - enriched in healthy
```{r gostplot2}
modulePick <- 1
myModule_down <- diffGenes[names(module.assign[module.assign %in% modulePick]),]
hrsub_down <- hclust(as.dist(1-cor(t(myModule_down), method="pearson")), method="complete")
gost.res_down <- gost(rownames(myModule_down), organism = "hsapiens", correction_method = "fdr")
gostplot(gost.res_down, interactive = T, capped = T) #set interactive=FALSE to get plot for publications
```
Column {data-width=500}
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### GSEA table
```{r GSEA}
hs_gsea_c2 <- msigdbr(species = "Homo sapiens", # change depending on species your data came from
category = "C2") %>% # choose your msigdb collection of interest
dplyr::select(gs_name, gene_symbol) #just get the columns corresponding to signature name and gene symbols of genes in each signature
# Now that you have your msigdb collections ready, prepare your data
# grab the dataframe you made in step3 script
# Pull out just the columns corresponding to gene symbols and LogFC for at least one pairwise comparison for the enrichment analysis
mydata.df.sub <- dplyr::select(mydata.df, geneID, LogFC)
mydata.gsea <- mydata.df.sub$LogFC
names(mydata.gsea) <- as.character(mydata.df.sub$geneID)
mydata.gsea <- sort(mydata.gsea, decreasing = TRUE)
# run GSEA using the 'GSEA' function from clusterProfiler
myGSEA.res <- GSEA(mydata.gsea, TERM2GENE=hs_gsea_c2, verbose=FALSE)
myGSEA.df <- as_tibble(myGSEA.res@result)
# view results as an interactive table
datatable(myGSEA.df,
extensions = c('KeyTable', "FixedHeader"),
caption = 'Signatures enriched in leishmaniasis',
options = list(keys = TRUE, searchHighlight = TRUE, pageLength = 10, lengthMenu = c("10", "25", "50", "100"))) %>%
formatRound(columns=c(3:10), digits=2)
```
About {data-orientation=rows data-icon="fa-comment-alt"}
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Row {data-height=1000}
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Use this page to describe the results in your own words. Some things to think about:
* What are the key takeaways from the analysis?
* What types of analyses would you want to do next?
* Based on your analysis, what wet-lab experiments would you pursue?
* How could you expand on or otherwise enhance this data dashboard?
